| Learning adversarial attack policies through multi-objective reinforcement learning J García, R Majadas, F Fernández Engineering Applications of Artificial Intelligence 96, 104021, 2020 | 18 | 2020 |
| Disturbing reinforcement learning agents with corrupted rewards R Majadas, J García, F Fernández arXiv preprint arXiv:2102.06587, 2021 | 6 | 2021 |
| Error analysis and correction for weighted A*’s suboptimality R Holte, R Majadas, A Pozanco, D Borrajo Proceedings of the International Symposium on Combinatorial Search 10 (1 …, 2019 | 2 | 2019 |
| Error Analysis and Correction for Weighted A*'s Suboptimality (Extended Version) RC Holte, R Majadas, A Pozanco, D Borrajo arXiv preprint arXiv:1905.11346, 2019 | 1 | 2019 |
| Clustering-based attack detection for adversarial reinforcement learning R Majadas, J García, F Fernández Applied Intelligence, 1-17, 2024 | | 2024 |
| Discrete uncertainty quantification for offline reinforcement learning JL Pérez, J Corrochano, J García, R Majadas, C Ibañez-Llano, S Pérez, ... Journal of Artificial Intelligence and Soft Computing Research 13 (4), 273-287, 2023 | | 2023 |
| Defending Against Adversarial Attacks on Policies Through Density Estimation A Villanueva, M Villacanas, R Majadas, J Garcıa, F Fernández FinPlan 2022, 1, 2022 | | 2022 |
| Resolución del juego Sokoban con técnicas de búsqueda R Majadas Sanz | | 2016 |
| Discrete Uncertainty Quantification Approach for Offline RL J Corrochano, J García, R Majadas, C Ibanez-Llano, S Pérez, ... | | |
Fernando FernándezProfesor Titular Universidad Carlos III de Madrid (UC3M)Verified email at inf.uc3m.es
